This section provides background information related to the present disclosure which is not necessarily prior art. This section further provides a general summary of the disclosure, and is not a comprehensive disclosure of its full scope or all of its features.
The emerging field of nanotechnology puts forth nanomaterials with desired mechanical, electrical, and chemical functionality at the macroscopic scale. Advances in the field have aided the development of multifunctional materials that are capable of achieving several functional goals within a single platform, such as displaying mechanical strength coupled with sensing and actuation capabilities. Much interest has surrounded the development of carbon nanotube (CNT) composites which are commonly composed of single-walled (SWNT) and multi-walled carbon nanotubes (MWNT) embedded within a polymer matrix. CNT composites take advantage of the impressive mechanical, electrical and physical properties of individual nanotubes to produce materials endowed with similar bulk properties. For example, mechanically strong CNT composites that simultaneously exhibit conductivity changes to external stimuli (i.e. to pH, strain, humidity and light) have been reported, thereby allowing the material to be used in a variety of sensing applications.
The development of multifunctional CNT composites for sensing applications hinges on accurate monitoring of the bulk electrical properties of the material before, during and after exposure to external stimuli. Various measurement techniques have been widely used to quantify the resistance of solid state and thin film materials. Most common are direct current (DC) techniques where a constant current, I, is injected into a sample while the voltage, V, across the sample is measured. If the points of current injection and voltage measurement are collocated, the approach is termed the two point probe method. If the sample resistance, R, is low, contact impedance of the probes can introduce measurement errors. To minimize the influence of contact impedance, the four-point probe method separates the points of current and voltage measurements to acquire a more accurate measurement of sample resistance. Using knowledge of the specimen geometry (e.g. film thickness), bulk material conductivity, σ, can be calculated using the measured resistance, R.
To more precisely analyze the electrical properties of materials whose resistance is dependent on the frequency of an applied alternating current (AC), electrical impedance spectroscopy (EIS) can be used in lieu of DC probe methods. In EIS, the amplitude and phase relationship between an applied AC signal and the measured voltage is encapsulated within an impedance measurement, Z(ω), where ω is the cyclic frequency of the injected current. Impedance is a complex valued electrical property of the specimen with the real and imaginary impedance components dependent upon the resistance and capacitance of the specimen, respectively.
An inherent limitation of both DC probe and EIS methods is the assumption of relative homogeneity of the specimen's electrical properties between probe points. Should the electrical properties of a specimen be spatially inhomogeneous, both methods average the spatial variations to provide an equivalent homogeneous resistance or impedance measurement. To offer spatial resolution of specimen resistance, adaptations of the DC probe methods have been offered. Scanning four-point probe methods have been proposed with probe measurements repeatedly made at equally spaced probe locations defined by a grid. This method can be scaled down into a scanning electron microscope (SEM) environment to offer a spatial resolution of a few microns.
Other established techniques include scanning spreading resistance microscopy which consists of a conductive atomic force microscope tip that measures spreading resistance profiles across a specimen cross section. While such methods provide a mapping of resistance over multiple dimensions, they require repeated time-consuming measurements that are conducted at the macro- or microscales. With the emergence of multifunctional thin film composites capable of sensing stimuli such as strain and pH, there is a need for analytical methods that can map the thin film resistance over multiple spatial dimensions. According to the principles of the present teachings, a novel approach to mapping the distribution of conductivity of CNT composites based upon electrical probing at the specimen boundary is described. Termed electrical impedance tomography (EIT), the method has been successfully applied in other fields including medical imaging of cancerous growths in animal tissue. The method differs from scanning probe methods since electrical measurements need only be taken at the boundary of a specimen. Based on voltage measurements taken at the film boundary when an AC signal is applied, EIT inversely solves the Laplace equation to reconstruct a two-dimensional (2D) map of the specimen conductivity within the measured film boundary. EIT is especially powerful in applications where the CNT composite is implemented as a sensing skin, since mapping of film conductivity provides direct spatial depictions of the external stimulus inducing the conductivity changes. According to the principles of the present teachings, the theory of conductivity reconstruction by EIT is presented followed by its implementation on layer-by-layer assembled carbon nanotube composite thin films. The method is validated by intentionally manufacturing films with defects consistent with non-uniform conductivity distributions. Furthermore, the use of nanotube-based composite thin films as a sensing skin is illustrated by mapping changes in film conductivity to different pH environments using EIT.
It should also be understood that the challenges associated with managing aging civil infrastructures, aerospace, and mechanical engineered systems demand new sensing technologies to ensure long-term safety and reliability. In addition, harsh operational and environmental conditions coupled with the lack of robust monitoring and maintenance schemes often accelerate structural degradation. In extreme cases, undetected structural damage such as impact damage, corrosion, and fatigue can lead to catastrophic system failure. For instance, undiagnosed corrosion of aircraft fuselages can result in over-stressing of rivets and stress-corrosion crack formation; an example of what can occur if damage is left undetected is the Aloha Airlines plane which ripped apart in-flight in 1988. More recently, impact damage compromised the integrity of the thermal insulation heat shield of the Columbia Space Shuttle which exploded during re-entry. In addition to such fatal catastrophic structural failures, an abundance of economic resources are dedicated to inspecting and repairing existing structures. For example, the United States Air Force Corrosion Prevention and Control Office (AFCPCO) estimates that more than $800 million is invested annually for corrosion maintenance of its aeronautical fleet. Even greater financial resources are invested annually to maintain the national inventory of bridges, pipelines, and other large civil infrastructure systems.
As a means to mitigate the economic burden of structural maintenance and the risk of catastrophic failures, the field of structural health monitoring (SHM) offers a diverse suite of sensing and algorithmic technologies that identify structural degradation so as to facilitate timely repairs. While many SHM techniques have been proposed, the majority relies on correlating changes in global structural properties to damage. In order to detect component level damage, techniques such as modal frequency analysis are often not sufficiently sensitive to local structural variations such as damage. While strain gages can be installed to obtain local structural measurements, their main disadvantage is that they can only provide response data at one discrete location in the structure. Accurate damage detection requires either a dense network of these point-sensors or accurate models for extrapolating localized strain fields to the remainder of the structure.
As opposed to relying on global-based SHM methods, guided-waves show tremendous promise for damage detection and localization in thin metallic structures. With ultrasonic waves able to propagate over long distances, this sensing approach provides the sensor with a large interrogation zone. For instance, Giurgiutiu, et al. bonds multiple arrays of surface-mounted piezoelectric transducers onto aluminum alloy aircraft panels for generating Lamb waves to detect cracks and corrosion damage. Using piezoelectric sensor/actuator responses, reflected echo signals are employed for accurate detection of damage due to hairline slits, seeded cracks, and impact. Similarly, Park, et al. has validated piezoelectric patches for identifying cracks developed in welded zones of ⅛-scale bridge truss members. Simulated damage by loose bolts and induced cracks are successfully detected via a Lamb-wave approach combined with a prescribed damage threshold index. On the other hand, when combining distributed piezoelectric sensors/actuators with a wavelet-based data processing technique, Sohn, et al. identifies the location of delamination within large 61×61 cm2 composite plates. While the aforementioned guided wave techniques are fairly robust for identifying damage in simple structural components, computational demand increases dramatically and accuracy decreases when structural geometries are complex and when multiple damage sites are within the active sensors' interrogation zone. In addition, a dense network of piezoelectric sensors and actuators, combined with computationally-intensive algorithms, are still required for detecting small damage features. Moreover, application of guided-waves on complex structures generally requires a priori geometrical knowledge of the undamaged structure to distinguish structural features from detected damage.
More recently, advances in nanotechnology have brought forth new materials and fabrication tools for developing high-sensitivity thin film sensors that are suitable for use in SHM. In fact, carbon fullerenes and nanotubes show tremendous potential for use in multifunctional materials. For example, it has been shown that single-walled carbon nanotubes (SWNT) possess a Young's Modulus of approximately 1 TPa and exhibit near ballistic transport-type electronic conductivities. The strength characteristics of SWNTs have led to many exploring their use for reinforcing polymeric and ceramic materials. While mechanical durability is one desirable attribute of carbon nanotube composites, another is the embedment of sensing functionalities. Specifically, the large surface area and high aspect ratio of SWNTs are ideally suited for chemical functionalization; binding molecules to the surface of SWNTs allow electromechanical and electrochemical sensing transduction mechanisms to be encoded into a carbon nanotube composite.
According to the principles of the present teachings, SWNTs are employed as a building block for the design and fabrication of multifunctional “sensing skins” that are capable of monitoring structural damage without having to probe multiple discrete sensor locations to infer the characteristics (i.e., type, location, and severity) of damage. The proposed sensing skin seeks to: 1) spatially image deformations (strain), 2) identify and locate foreign object and blast debris impact, and 3) observe the formation of corrosion byproducts. Using a layer-by-layer (LbL) self-assembly process, individual SWNTs are functionalized with various polyelectrolyte species for embedding multiple sensing transduction mechanisms within the composite's morphology. Here, SWNT-based LbL nanocomposites are embedded with strain sensitivity and corrosion detection capabilities. It will be shown that applied strain, impact damage, and corrosion byproduct formation alter thin film conductivity, thereby enabling a multifunctional composite capable of monitoring common structural damage processes. Unlike other carbon nanotube-based films cast by vacuum filtration, thermal evaporation, annealing, or epoxy-molding, LbL-based nanocomposites exhibit high deformation tolerance (up to 10,000με), robust field survivability, and highly uniform morphology. Realization of a sensing skin with spatial resolution is achieved by coupling the nanocomposites with an electrical impedance tomographic (EIT) spatial conductivity imaging technique. In short, the EIT conductivity mapping technique relies on repeated electrical measurements at the film boundary so as to inversely reconstruct the skin's spatial conductivity distribution. Since the LbL-based sensing skin is embedded with specific sensing transduction mechanisms, changes in film conductivity can be directly correlated to strain, impact, corrosion, pH, among others.
The present disclosure begins with a brief introduction to electrical impedance tomography. Then, the layer-by-layer skin fabrication process is described in detail. Finally, the carbon nanotube sensing skins are experimentally validated to detect tensile-compressive cyclic strains, impact damage, and corrosion byproduct formation in typical metallic structural components.
Further areas of applicability will become apparent from the description provided herein. The description and specific examples in this summary are intended for purposes of illustration only and are not intended to limit the scope of the present disclosure.